Video Representation Learning Self-supervised methods have dominated much of the progress in multi-modal media understanding in recent years [1]}, [2]}, [3]}, [4]}. CLIP [5]} was an early example of achieving impressive zero-shot visual classification following self-supervised training with over 400M image-caption pairs. Similar advances have been made for audio [6]} and video [7]} by utilizing different augmented views of the same modality [8]}, [9]}, [10]}, [11]}, or by learning joint embeddings of short [6]}, [13]}, [7]} or long-form [15]} videos. Our system leverages such work for learning video representations that capture matching video pairs for the task of match cutting.
